goat<-read.table("../data/goat.csv")
goatt<-t(goat)
library("corrplot")
## corrplot 0.90 loaded
library("FactoMineR")
library("factoextra")
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library("reshape2")
library("ggplot2")
library("Hmisc")
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
summary(goat)
## cd4_goat1 cd4_goat2 cd4_goat3
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.672 Median : 0.797 Median : 0.857
## Mean : 33.777 Mean : 35.090 Mean : 32.679
## 3rd Qu.: 20.914 3rd Qu.: 20.940 3rd Qu.: 19.929
## Max. :12587.386 Max. :14665.324 Max. :12121.344
## cd4_goat4 cd8_goat1 cd8_goat2 cd8_goat3
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.777 Median : 0.787 Median : 0.85 Median : 0.898
## Mean : 32.408 Mean : 34.065 Mean : 34.76 Mean : 34.289
## 3rd Qu.: 20.428 3rd Qu.: 21.132 3rd Qu.: 21.23 3rd Qu.: 20.485
## Max. :11393.197 Max. :12622.673 Max. :13835.64 Max. :12630.834
## cd8_goat4 cerebellum_goat1 cerebellum_goat2
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.030 1st Qu.: 0.033
## Median : 0.839 Median : 2.713 Median : 2.580
## Mean : 33.944 Mean : 31.835 Mean : 30.986
## 3rd Qu.: 20.865 3rd Qu.: 22.960 3rd Qu.: 22.700
## Max. :10231.537 Max. :20765.002 Max. :18409.420
## cerebellum_goat3 cerebellum_goat4 ileum_goat4
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.036 1st Qu.: 0.037 1st Qu.: 0.029
## Median : 2.759 Median : 2.897 Median : 2.765
## Mean : 31.843 Mean : 31.575 Mean : 35.768
## 3rd Qu.: 23.463 3rd Qu.: 23.958 3rd Qu.: 24.055
## Max. :19318.600 Max. :18025.833 Max. :13249.520
## liver_goat1 liver_goat2 liver_goat3 liver_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.02 1st Qu.: 0.02 1st Qu.: 0.01 1st Qu.: 0.02
## Median : 2.62 Median : 2.76 Median : 2.21 Median : 2.83
## Mean : 72.29 Mean : 74.50 Mean : 64.65 Mean : 65.60
## 3rd Qu.: 25.68 3rd Qu.: 25.19 3rd Qu.: 25.36 3rd Qu.: 25.87
## Max. :129798.50 Max. :165934.97 Max. :97641.67 Max. :67000.18
## lung_goat1 lung_goat2 lung_goat3 lung_goat4
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.040 1st Qu.: 0.036 1st Qu.: 0.040 1st Qu.: 0.033
## Median : 3.763 Median : 3.487 Median : 3.756 Median : 3.591
## Mean : 33.650 Mean : 34.410 Mean : 33.577 Mean : 32.588
## 3rd Qu.: 25.210 3rd Qu.: 25.019 3rd Qu.: 24.903 3rd Qu.: 24.499
## Max. :9130.728 Max. :8163.974 Max. :9260.541 Max. :8023.443
## muscle_goat1 muscle_goat2 muscle_goat3 muscle_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 1.63 Median : 1.32 Median : 1.09 Median : 1.41
## Mean : 92.10 Mean : 82.52 Mean : 88.09 Mean : 75.06
## 3rd Qu.: 21.96 3rd Qu.: 21.36 3rd Qu.: 21.09 3rd Qu.: 21.26
## Max. :107851.12 Max. :95552.58 Max. :168102.68 Max. :69647.12
## testis_goat1 testis_goat2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.213 1st Qu.: 0.217
## Median : 3.963 Median : 3.917
## Mean : 36.527 Mean : 33.845
## 3rd Qu.: 27.942 3rd Qu.: 26.466
## Max. :3268.795 Max. :3054.008
boxplot(goat)
boxplot(goat,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat==0)/nrow(goat))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
goat50=goatt[,1:50]
lapply(1:ncol(goat50), function(i) hist(goat50[,i],xlab="Expression",main=paste("Histogram of" ,colnames(goat50)[i])))
## [[1]]
## $breaks
## [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
##
## $counts
## [1] 22 1 0 0 1 1 1 1
##
## $density
## [1] 1.62962963 0.07407407 0.00000000 0.00000000 0.07407407 0.07407407 0.07407407
## [8] 0.07407407
##
## $mids
## [1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[2]]
## $breaks
## [1] 0 500 1000 1500 2000
##
## $counts
## [1] 21 3 1 2
##
## $density
## [1] 1.555556e-03 2.222222e-04 7.407407e-05 1.481481e-04
##
## $mids
## [1] 250 750 1250 1750
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[3]]
## $breaks
## [1] 0 2 4 6 8 10 12 14
##
## $counts
## [1] 23 0 0 0 0 2 2
##
## $density
## [1] 0.42592593 0.00000000 0.00000000 0.00000000 0.00000000 0.03703704 0.03703704
##
## $mids
## [1] 1 3 5 7 9 11 13
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[4]]
## $breaks
## [1] 0 500 1000 1500 2000 2500 3000
##
## $counts
## [1] 14 5 0 5 2 1
##
## $density
## [1] 1.037037e-03 3.703704e-04 0.000000e+00 3.703704e-04 1.481481e-04
## [6] 7.407407e-05
##
## $mids
## [1] 250 750 1250 1750 2250 2750
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[5]]
## $breaks
## [1] 0 20 40 60 80 100 120 140
##
## $counts
## [1] 19 0 3 1 0 2 2
##
## $density
## [1] 0.035185185 0.000000000 0.005555556 0.001851852 0.000000000 0.003703704
## [7] 0.003703704
##
## $mids
## [1] 10 30 50 70 90 110 130
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[6]]
## $breaks
## [1] 0 500 1000 1500 2000 2500
##
## $counts
## [1] 11 8 4 2 2
##
## $density
## [1] 0.0008148148 0.0005925926 0.0002962963 0.0001481481 0.0001481481
##
## $mids
## [1] 250 750 1250 1750 2250
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[7]]
## $breaks
## [1] 0 5 10 15 20
##
## $counts
## [1] 13 5 7 2
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## $density
## [1] 0.09629630 0.03703704 0.05185185 0.01481481
##
## $mids
## [1] 2.5 7.5 12.5 17.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[8]]
## $breaks
## [1] 0 5 10 15 20 25
##
## $counts
## [1] 12 3 7 4 1
##
## $density
## [1] 0.088888889 0.022222222 0.051851852 0.029629630 0.007407407
##
## $mids
## [1] 2.5 7.5 12.5 17.5 22.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[9]]
## $breaks
## [1] 0 10 20 30 40 50 60 70
##
## $counts
## [1] 13 8 2 0 2 1 1
##
## $density
## [1] 0.048148148 0.029629630 0.007407407 0.000000000 0.007407407 0.003703704
## [7] 0.003703704
##
## $mids
## [1] 5 15 25 35 45 55 65
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[10]]
## $breaks
## [1] 0 1000 2000 3000 4000 5000 6000 7000
##
## $counts
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## $density
## [1] 7.407407e-05 1.111111e-04 2.592593e-04 1.851852e-04 7.407407e-05
## [6] 2.222222e-04 7.407407e-05
##
## $mids
## [1] 500 1500 2500 3500 4500 5500 6500
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
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## attr(,"class")
## [1] "histogram"
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## [[11]]
## $breaks
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## $density
## [1] 0.62962963 0.18518519 0.11111111 0.00000000 0.03703704 0.03703704
##
## $mids
## [1] 0.5 1.5 2.5 3.5 4.5 5.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[12]]
## $breaks
## [1] 0 1 2 3 4 5
##
## $counts
## [1] 17 5 3 1 1
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## $density
## [1] 0.62962963 0.18518519 0.11111111 0.03703704 0.03703704
##
## $mids
## [1] 0.5 1.5 2.5 3.5 4.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
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## attr(,"class")
## [1] "histogram"
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## [[13]]
## $breaks
## [1] 0.0 0.5 1.0 1.5 2.0
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## $counts
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## $density
## [1] 1.25925926 0.51851852 0.07407407 0.14814815
##
## $mids
## [1] 0.25 0.75 1.25 1.75
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## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[14]]
## $breaks
## [1] 0 5 10 15 20 25
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## $counts
## [1] 13 9 2 1 2
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## $density
## [1] 0.096296296 0.066666667 0.014814815 0.007407407 0.014814815
##
## $mids
## [1] 2.5 7.5 12.5 17.5 22.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[15]]
## $breaks
## [1] 0 10 20 30 40 50 60 70
##
## $counts
## [1] 11 7 6 1 0 1 1
##
## $density
## [1] 0.040740741 0.025925926 0.022222222 0.003703704 0.000000000 0.003703704
## [7] 0.003703704
##
## $mids
## [1] 5 15 25 35 45 55 65
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[16]]
## $breaks
## [1] 0 10000 20000 30000 40000 50000
##
## $counts
## [1] 12 8 3 2 2
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## $density
## [1] 4.444444e-05 2.962963e-05 1.111111e-05 7.407407e-06 7.407407e-06
##
## $mids
## [1] 5000 15000 25000 35000 45000
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[17]]
## $breaks
## [1] 0 100 200 300 400 500 600 700
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## $counts
## [1] 6 10 5 1 3 0 2
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## $density
## [1] 0.0022222222 0.0037037037 0.0018518519 0.0003703704 0.0011111111
## [6] 0.0000000000 0.0007407407
##
## $mids
## [1] 50 150 250 350 450 550 650
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[18]]
## $breaks
## [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
##
## $counts
## [1] 4 11 6 2 2 0 1 1
##
## $density
## [1] 1.4814815 4.0740741 2.2222222 0.7407407 0.7407407 0.0000000 0.3703704
## [8] 0.3703704
##
## $mids
## [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[19]]
## $breaks
## [1] 0 500 1000 1500 2000 2500
##
## $counts
## [1] 14 9 0 3 1
##
## $density
## [1] 1.037037e-03 6.666667e-04 0.000000e+00 2.222222e-04 7.407407e-05
##
## $mids
## [1] 250 750 1250 1750 2250
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[20]]
## $breaks
## [1] 0.00 0.02 0.04 0.06 0.08 0.10 0.12
##
## $counts
## [1] 20 1 0 2 2 2
##
## $density
## [1] 37.037037 1.851852 0.000000 3.703704 3.703704 3.703704
##
## $mids
## [1] 0.01 0.03 0.05 0.07 0.09 0.11
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[21]]
## $breaks
## [1] 0 10 20 30 40 50 60 70
##
## $counts
## [1] 2 12 4 4 2 1 2
##
## $density
## [1] 0.007407407 0.044444444 0.014814815 0.014814815 0.007407407 0.003703704
## [7] 0.007407407
##
## $mids
## [1] 5 15 25 35 45 55 65
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[22]]
## $breaks
## [1] 0 500 1000 1500 2000 2500 3000 3500
##
## $counts
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##
## $density
## [1] 5.185185e-04 8.148148e-04 2.962963e-04 7.407407e-05 1.481481e-04
## [6] 7.407407e-05 7.407407e-05
##
## $mids
## [1] 250 750 1250 1750 2250 2750 3250
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[23]]
## $breaks
## [1] 0 1000 2000 3000 4000 5000 6000 7000 8000
##
## $counts
## [1] 3 12 3 3 2 2 1 1
##
## $density
## [1] 1.111111e-04 4.444444e-04 1.111111e-04 1.111111e-04 7.407407e-05
## [6] 7.407407e-05 3.703704e-05 3.703704e-05
##
## $mids
## [1] 500 1500 2500 3500 4500 5500 6500 7500
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[24]]
## $breaks
## [1] 0 5 10 15 20 25
##
## $counts
## [1] 20 3 1 2 1
##
## $density
## [1] 0.148148148 0.022222222 0.007407407 0.014814815 0.007407407
##
## $mids
## [1] 2.5 7.5 12.5 17.5 22.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[25]]
## $breaks
## [1] 0 200 400 600 800 1000 1200 1400 1600 1800
##
## $counts
## [1] 5 11 3 1 3 0 1 0 3
##
## $density
## [1] 0.0009259259 0.0020370370 0.0005555556 0.0001851852 0.0005555556
## [6] 0.0000000000 0.0001851852 0.0000000000 0.0005555556
##
## $mids
## [1] 100 300 500 700 900 1100 1300 1500 1700
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[26]]
## $breaks
## [1] 0 1 2 3 4 5 6 7
##
## $counts
## [1] 9 12 2 0 2 0 2
##
## $density
## [1] 0.33333333 0.44444444 0.07407407 0.00000000 0.07407407 0.00000000 0.07407407
##
## $mids
## [1] 0.5 1.5 2.5 3.5 4.5 5.5 6.5
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[27]]
## $breaks
## [1] 0 500 1000 1500 2000
##
## $counts
## [1] 8 13 3 3
##
## $density
## [1] 0.0005925926 0.0009629630 0.0002222222 0.0002222222
##
## $mids
## [1] 250 750 1250 1750
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[28]]
## $breaks
## [1] 0 2000 4000 6000 8000 10000
##
## $counts
## [1] 5 13 5 1 3
##
## $density
## [1] 9.259259e-05 2.407407e-04 9.259259e-05 1.851852e-05 5.555556e-05
##
## $mids
## [1] 1000 3000 5000 7000 9000
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[29]]
## $breaks
## [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
##
## $counts
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##
## $density
## [1] 4.444444 2.222222 3.703704 2.222222 2.962963 2.962963 1.481481
##
## $mids
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##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[30]]
## $breaks
## [1] 0.00 0.02 0.04 0.06 0.08 0.10 0.12
##
## $counts
## [1] 19 5 2 0 0 1
##
## $density
## [1] 35.185185 9.259259 3.703704 0.000000 0.000000 1.851852
##
## $mids
## [1] 0.01 0.03 0.05 0.07 0.09 0.11
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[31]]
## $breaks
## [1] 0.0 0.2 0.4 0.6 0.8 1.0
##
## $counts
## [1] 23 0 1 2 1
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## $density
## [1] 4.2592593 0.0000000 0.1851852 0.3703704 0.1851852
##
## $mids
## [1] 0.1 0.3 0.5 0.7 0.9
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[32]]
## $breaks
## [1] 0 1000 2000 3000 4000 5000 6000
##
## $counts
## [1] 11 8 4 0 3 1
##
## $density
## [1] 4.074074e-04 2.962963e-04 1.481481e-04 0.000000e+00 1.111111e-04
## [6] 3.703704e-05
##
## $mids
## [1] 500 1500 2500 3500 4500 5500
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[33]]
## $breaks
## [1] 0 100 200 300 400 500 600
##
## $counts
## [1] 16 3 4 1 2 1
##
## $density
## [1] 0.0059259259 0.0011111111 0.0014814815 0.0003703704 0.0007407407
## [6] 0.0003703704
##
## $mids
## [1] 50 150 250 350 450 550
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[34]]
## $breaks
## [1] 0 20 40 60 80 100
##
## $counts
## [1] 18 1 6 1 1
##
## $density
## [1] 0.033333333 0.001851852 0.011111111 0.001851852 0.001851852
##
## $mids
## [1] 10 30 50 70 90
##
## $xname
## [1] "goat50[, i]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## [[35]]
## $breaks
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tissue_split <- strsplit(colnames(goat),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})
goat2 <- goat
colnames(goat2) <- tissue
goat2 <- melt(as.matrix(goat2))
colnames(goat2) <- c("Gene","Tissue","Expression")
ggplot(data=goat2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
eps=1e-6
ggplot(data=goat2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
goat_acp<-PCA(goatt,graph=FALSE) #the individuals are the tissues and the variables are the genes
fviz_pca_ind (goat_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)
## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_acp, addlabels = TRUE) #to visualize how many components are necessary
corrplot(cor(goat),method="ellipse")
corrplot(cor(goatt[,1:30]),method="ellipse")
corrplot(cor(goatt[,1:50]),method="ellipse")
corrplot(cor(goatt[,1000:1030]),method="ellipse")
goat_loess<-read.table("../data/goat_loess.csv")
goat_loesst<-t(goat_loess)
summary(goat_loess)
## cd4_goat1 cd4_goat2 cd4_goat3 cd4_goat4
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.42 Median : 1.579 Median : 1.697 Median : 1.559
## Mean : 29.01 Mean : 29.597 Mean : 28.741 Mean : 28.642
## 3rd Qu.: 23.48 3rd Qu.: 23.147 3rd Qu.: 21.917 3rd Qu.: 22.467
## Max. :6702.30 Max. :6607.418 Max. :6546.953 Max. :7038.558
## cd8_goat1 cd8_goat2 cd8_goat3 cd8_goat4
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.45 Median : 1.516 Median : 1.624 Median : 1.573
## Mean : 28.52 Mean : 28.861 Mean : 29.751 Mean : 29.785
## 3rd Qu.: 22.75 3rd Qu.: 22.715 3rd Qu.: 22.511 3rd Qu.: 22.920
## Max. :6067.88 Max. :6179.076 Max. :6404.083 Max. :6400.909
## cerebellum_goat1 cerebellum_goat2 cerebellum_goat3 cerebellum_goat4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.03 1st Qu.: 0.03 1st Qu.: 0.03 1st Qu.: 0.03
## Median : 1.93 Median : 1.92 Median : 1.92 Median : 1.93
## Mean : 43.37 Mean : 44.64 Mean : 43.92 Mean : 40.39
## 3rd Qu.: 22.71 3rd Qu.: 22.93 3rd Qu.: 22.84 3rd Qu.: 22.65
## Max. :80599.58 Max. :89199.93 Max. :83855.13 Max. :62475.01
## ileum_goat4 liver_goat1 liver_goat2 liver_goat3
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.032 1st Qu.: 0.02 1st Qu.: 0.02 1st Qu.: 0.01
## Median : 2.358 Median : 2.14 Median : 2.23 Median : 2.09
## Mean : 36.352 Mean : 61.20 Mean : 64.70 Mean : 58.74
## 3rd Qu.: 23.983 3rd Qu.: 23.68 3rd Qu.: 23.44 3rd Qu.: 23.99
## Max. :15633.909 Max. :78155.61 Max. :100416.48 Max. :74426.64
## liver_goat4 lung_goat1 lung_goat2 lung_goat3
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.02 1st Qu.: 0.030 1st Qu.: 0.025 1st Qu.: 0.028
## Median : 2.20 Median : 2.189 Median : 2.133 Median : 2.279
## Mean : 60.04 Mean : 29.228 Mean : 30.161 Mean : 29.716
## 3rd Qu.: 23.29 3rd Qu.: 21.674 3rd Qu.: 21.617 3rd Qu.: 21.831
## Max. :56324.83 Max. :7720.992 Max. :7011.222 Max. :7485.892
## lung_goat4 muscle_goat1 muscle_goat2 muscle_goat3
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.029 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 2.275 Median : 1.93 Median : 1.79 Median : 1.64
## Mean : 30.360 Mean : 63.79 Mean : 63.24 Mean : 66.46
## 3rd Qu.: 22.030 3rd Qu.: 23.08 3rd Qu.: 23.79 3rd Qu.: 24.72
## Max. :8257.394 Max. :38837.86 Max. :40558.02 Max. :72589.46
## muscle_goat4 testis_goat1 testis_goat2
## Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.04 1st Qu.: 0.05
## Median : 1.835 Median : 2.37 Median : 2.40
## Mean : 57.033 Mean : 63.51 Mean : 64.39
## 3rd Qu.: 23.789 3rd Qu.: 27.00 3rd Qu.: 26.67
## Max. :28664.987 Max. :65135.92 Max. :77294.82
boxplot(goat_loess)
boxplot(goat_loess,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat_loess==0)/nrow(goat_loess))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat_loess,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
tissue_split <- strsplit(colnames(goat),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})
goat_loess2 <- goat_loess
colnames(goat_loess2) <- tissue
goat_loess2 <- melt(as.matrix(goat_loess2))
colnames(goat_loess2) <- c("Gene","Tissue","Expression")
ggplot(data=goat_loess2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+labs(title="goat_loess tissues boxplots")+ theme(legend.position="none")
When choosing to fill in colors the outliers on red, all points are colored in red less the horizontal bar on level 0.
eps=1e-6
ggplot(data=goat_loess2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+labs(title="goat_loess tissues boxplots")+ theme(legend.position="none")
goat_loess_acp<-PCA(goat_loesst,graph=FALSE) #the individuals are the tissues and the variables are the genes
fviz_pca_ind (goat_loess_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_loess_acp, addlabels = TRUE) #to visualize how many components are necessary
corrplot(cor(goat),method="ellipse")
corrplot(cor(goat_loess),method="ellipse")
goat_tpm<-read.table("../data/goat_tpm.csv")
#transformations needed to have the same matrix structure that other goats data
#lines
rownames(goat_tpm)<-as.character(goat_tpm[,1])
#colomns
colnames(goat_tpm)<-as.character(goat_tpm[1,])
goat_tpm<-goat_tpm[c(-1),c(-1)]
goat_tpm<-type.convert(goat_tpm) #by defaul numbers were as characters instead of numeric
goat_tpmt<-t(goat_tpm)
boxplot(goat_tpm)
boxplot(goat_tpm,outline=FALSE) #outliers are not drawn
#percentage of 0
percent_zero<-(colSums(goat_tpm==0)/nrow(goat_tpm))*100
plot(percent_zero,col="purple",main="Percentage of 0 per tissue and subject",type="b")
#median
list_median<-apply(goat_tpm,2,median)
plot(list_median,col="blue",main="Median per tissue and subject",type="b",xlab="Tissues")
tissue_split <- strsplit(colnames(goat_tpm),"_")
tissue <- sapply(tissue_split,function(x){return (x[1])})
goat_tpm2 <- goat_tpm
colnames(goat_tpm2) <- tissue
goat_tpm2 <- melt(as.matrix(goat_tpm2))
colnames(goat_tpm2) <- c("Gene","Tissue","Expression")
ggplot(data=goat_tpm2,aes(x=Tissue,y=Expression,color=Tissue))+geom_boxplot()+labs(title="goat_tpm tissues boxplots")+ theme(legend.position="none")
eps=1e-6
ggplot(data=goat_tpm2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+labs(title="goat_tpm tissues boxplots")+ theme(legend.position="none")
library("FactoMineR")
library("factoextra")
goat_tpm_acp<-PCA(goat_tpmt,graph=FALSE) #the individuals are the tissues and the variables are the genes
fviz_pca_ind (goat_tpm_acp, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Évite le chevauchement de texte
)
## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_eig(goat_tpm_acp, addlabels = TRUE) #to visualize how many components are necessary
corrplot(cor(goat),method="ellipse")
corrplot(cor(goat_loess),method="ellipse")
corrplot(cor(goat_tpm),method="ellipse")
A significant difference is testis that is correlated to several tissues
and mostly the cerebellum. LIver and muscle not significantly correlated
to others in all three normalizations.
par(mfrow=c(1,3))
ind_goat<-get_pca_ind(goat_acp)
corrplot(ind_goat$cos2, is.corr=FALSE,title = "TMM normalization",mar=c(0,0,2,0))
ind_goat_loess<-get_pca_ind(goat_loess_acp)
corrplot(ind_goat_loess$cos2, is.corr=FALSE,title = "Loess normalization",mar=c(0,0,2,0))
ind_goat_tpm<-get_pca_ind(goat_tpm_acp)
corrplot(ind_goat_tpm$cos2, is.corr=FALSE,title = "TPM normalization",mar=c(0,0,2,0))
For all normalizations testis represent around 80% or more dimension 1. For the others, we have essenciall cerebellum ans lung that contribute fort the other dimensions.
par(mfrow=c(2,2))
eps=1e-6
ggplot(data=goat2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat tissues boxplots")+theme(legend.position="none")
ggplot(data=goat_loess2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat_loess tissues boxplots")+theme(legend.position="none")
ggplot(data=goat_tpm2,aes(x=Tissue,y=log(Expression+eps),color=Tissue))+geom_boxplot()+ labs(title="Goat_tpm tissues boxplots")+theme(legend.position="none")
par(mfrow=c(1,3))
fviz_eig(goat_acp, addlabels = TRUE) #to visualize how many components are necessary
fviz_eig(goat_loess_acp, addlabels = TRUE) #to visualize how many components are necessary
fviz_eig(goat_tpm_acp, addlabels = TRUE) #to visualize how many components are necessary
sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Hmisc_4.5-0 Formula_1.2-4 survival_3.2-11 lattice_0.20-44
## [5] reshape2_1.4.4 factoextra_1.0.7 ggplot2_3.3.5 FactoMineR_2.4
## [9] corrplot_0.90
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.0 tidyr_1.1.3 jsonlite_1.7.2
## [4] splines_4.0.4 carData_3.0-4 bslib_0.2.5.1
## [7] assertthat_0.2.1 highr_0.9 latticeExtra_0.6-29
## [10] cellranger_1.1.0 yaml_2.2.1 ggrepel_0.9.1
## [13] pillar_1.6.1 backports_1.2.1 glue_1.4.2
## [16] digest_0.6.27 RColorBrewer_1.1-2 ggsignif_0.6.2
## [19] checkmate_2.0.0 colorspace_2.0-2 htmltools_0.5.1.1
## [22] Matrix_1.3-3 plyr_1.8.6 pkgconfig_2.0.3
## [25] broom_0.7.8 haven_2.4.1 purrr_0.3.4
## [28] scales_1.1.1 openxlsx_4.2.4 jpeg_0.1-8.1
## [31] rio_0.5.27 htmlTable_2.2.1 tibble_3.1.2
## [34] car_3.0-11 generics_0.1.0 farver_2.1.0
## [37] ellipsis_0.3.2 ggpubr_0.4.0 DT_0.18
## [40] withr_2.4.2 nnet_7.3-16 readxl_1.3.1
## [43] magrittr_2.0.1 crayon_1.4.1 evaluate_0.14
## [46] fansi_0.5.0 MASS_7.3-54 forcats_0.5.1
## [49] rstatix_0.7.0 foreign_0.8-81 tools_4.0.4
## [52] data.table_1.14.0 hms_1.1.0 lifecycle_1.0.0
## [55] stringr_1.4.0 munsell_0.5.0 zip_2.2.0
## [58] cluster_2.1.2 flashClust_1.01-2 compiler_4.0.4
## [61] jquerylib_0.1.4 rlang_0.4.11 grid_4.0.4
## [64] rstudioapi_0.13 htmlwidgets_1.5.3 leaps_3.1
## [67] base64enc_0.1-3 labeling_0.4.2 rmarkdown_2.14
## [70] gtable_0.3.0 abind_1.4-5 curl_4.3.2
## [73] DBI_1.1.1 R6_2.5.0 gridExtra_2.3
## [76] knitr_1.33 dplyr_1.0.7 utf8_1.2.1
## [79] stringi_1.6.2 Rcpp_1.0.7 vctrs_0.3.8
## [82] rpart_4.1-15 png_0.1-7 scatterplot3d_0.3-41
## [85] tidyselect_1.1.1 xfun_0.31